Abstract
Background
Previous evidence suggests that cardiologists and family doctors have limited accuracy
in predicting patient prognosis. Predictive models with satisfactory accuracy for
estimating mortality in patients with heart failure (HF) exist; physicians, however,
seldom use these models. We evaluated the relative accuracy of physician vs model
prediction to estimate 1-year survival in ambulatory patients with HF.
Methods
We conducted a single-centre cross-sectional study involving 150 consecutive ambulatory
patients with HF >18 years of age with a left ventricular ejection fraction ≤40%.
Each patient’s cardiologist and family doctor provided their predicted 1-year survival,
and predicted survival scores were calculated using 3 models: HF Meta-Score, Seattle
Heart Failure Model (SHFM), and Meta-Analysis Global Group in Chronic HF (MAGGIC)
score. We compared accuracy between physician and model predictions using intraclass
correlation (ICC).
Results
Median predicted survival by HF cardiologists was lower (median 80%, interquartile
range [IQR]: 61%-90%) than that predicted by family physicians (median 90%, IQR 70%-99%,
P = 0.08). One-year median survival calculated by the HF Meta-Score (94.6%), SHFM (95.4%),
and MAGGIC (88.9%,) proved as high or higher than physician estimates. Agreement among
HF cardiologists (ICC 0.28-0.41) and family physicians (ICC 0.43-0.47) when compared
with 1-year model-predicted survival scores proved limited, whereas the 3 models agreed
well (ICC > 0.65).
Conclusions
HF cardiologists underestimated survival in comparison with family physicians, whereas
both physician estimates were lower than calculated model estimates. Our results provide
additional evidence of potential inaccuracy of physician survival predictions in ambulatory
patients with HF. These results should be validated in longitudinal studies collecting
actual survival.
Résumé
Contexte
Les données issues d’études antérieures indiquent que le pronostic établi par les
cardiologues et les médecins de famille est rarement précis. Il existe des modèles
prédictifs d’une précision satisfaisante pour estimer la mortalité chez les patients
présentant une insuffisance cardiaque (IC), mais rares sont les médecins qui les utilisent.
Nous avons comparé la précision relative des prédictions faites par les médecins à
celles obtenues à l’aide de modèles à l’égard de l’estimation de la survie à 1 an
chez des patients ambulatoires présentant une IC.
Méthodologie
Nous avons mené une étude transversale unicentrique sur les cas de 150 patients ambulatoires
consécutifs âgés de > 18 ans présentant une IC et une fraction d’éjection ventriculaire
gauche ≤ 40 %. Le cardiologue et le médecin de famille de chacun des patients ont
évalué la survie à 1 an, et nous avons calculé les scores de survie prédite à l'aide
de 3 modèles : HF Meta-Score, SHFM (Seattle Heart Failure Model) et MAGGIC (Meta-Analysis Global Group in Chronic HF). Nous avons ensuite comparé la précision des prédictions des médecins avec celle
des prédictions des modèles en calculant la corrélation intraclasse (CIC).
Résultats
La survie médiane prédite par les cardiologues spécialisés en IC était inférieure
(médiane de 80 %, intervalle interquartile [IIQ] : de 61 % à 90 %) à celle prédite
par les médecins de famille (médiane de 90 %, IIQ : de 70 % à 99 %, p = 0,08). La survie médiane à 1 an calculée au moyen des modèles HF Meta-Score (94,6
%), SHFM (95,4 %) et MAGGIC (88,9 %) était équivalente ou supérieure aux estimations
des médecins. La concordance entre les prédictions des cardiologues spécialisés en
IC (CIC : de 0,28 à 0,41) et des médecins de famille (CIC : de 0,43 à 0,47) et les
scores de survie à 1 an prédits par les modèles était faible, tandis que les prédictions
des trois modèles concordaient bien (CIC > 0,65).
Conclusions
Les cardiologues spécialisés en IC ont sous-estimé la survie par rapport aux médecins
de famille, tandis que les deux groupes de médecins ont fait des prédictions inférieures
à celles des modèles. Les résultats de l'étude fournissent des preuves supplémentaires
de l'inexactitude potentielle des prédictions de la survie faites par les médecins
chez les patients ambulatoires présentant une IC. Il conviendrait de valider ces résultats
au moyen d'études longitudinales comportant des données sur la survie réelle.
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References
- Heart Disease and Stroke Statistics–2016 Update: a report from the American Heart Association.Circulation. 2016; 133: e38-e360
- Canadian Cardiovascular Outcomes Research Team. Regional variation in self-reported heart disease prevalence in Canada.Can J Cardiol. 2005; 21: 1265-1271
- Heart and Stroke Foundation. Report on the Health of Canadians: The Burden of Heart Failure.2016 (Available at:)http://www.heartandstroke.ca/-/media/pdf-files/canada/2017-heart-month/heartandstroke-reportonhealth-2016.ashx?la=enDate accessed: August 30, 2017
- Epidemiology and risk profile of heart failure.Nat Rev Cardiol. 2011; 8: 30-41
- Trends in death attributed to myocardial infarction, heart failure and pulmonary embolism in Europe and Canada over the last decade.QJM. 2014; 107: 813-820
- Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review.Circ Heart Fail. 2013; 6: 881-889
- Impact of a clinical decision rule on hospital triage of patients with suspected acute cardiac ischemia in the emergency department.JAMA. 2002; 288: 342-350
- Risk prediction in patients with heart failure: a systematic review and analysis.J Am Coll Cardiol Heart Fail. 2014; 2: 440-446
- Users' guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group.JAMA. 2000; 284: 79-84
- The pneumonia severity index: a decade after the initial derivation and validation.Clin Infect Dis. 2008; 47: S133-S139
- "Surgeons' intuition" versus "prognostic models": predicting the risk of liver resections.Ann Surg. 2014; 260 (discussion 928-30): 923-928
- Clinical versus actuarial judgment.Science. 1989; 243: 1668-1674
- Clinical versus statistical prediction: the contribution of Paul E. Meehl.J Clin Psychol. 2005; 61: 1233-1243
- Perceived risk of ischemic and bleeding events in acute coronary syndromes.Circ Cardiovasc Qual Outcomes. 2013; 6: 299-308
- Discordant perceptions of prognosis and treatment options between physicians and patients with advanced heart failure.J Am Coll Cardiol Heart Fail. 2017; 5: 663-671
- One-year mortality among unselected outpatients with heart failure.Eur Heart J. 2002; 23: 1861-1866
- The Seattle Heart Failure Model: prediction of survival in heart failure.Circulation. 2006; 113: 1424-1433
- Predicting survival in patients with heart failure with an implantable cardioverter defibrillator: the Heart Failure Meta-Score.J Card Fail. 2018; 24: 735-745
- Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies.Eur Heart J. 2013; 34: 1404-1413
- Discordance between patient-predicted and model-predicted life expectancy among ambulatory patients with heart failure.JAMA. 2008; 299: 2533-2542
- Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker.Heart. 2012; 98: 683-690
- Discussing prognosis in feart failure: a questionnaire-based study of the patient's view.J Am Coll Cardiol Heart Fail. 2018; 6: 803-804
- Patient-centred care as an approach to improving health care in Australia.Collegian. 2018; 25: 119-123
- Perspective: the negativity bias, medical education, and the culture of academic medicine: why culture change is hard.Acad Med. 2012; 87: 1205-1209
- The evolution of the master diagnostician.JAMA. 2013; 310: 579-580
- Performance of prognostic risk scores in chronic heart failure patients enrolled in the European Society of Cardiology Heart Failure Long-Term Registry.J Am Coll Cardiol Heart Fail. 2018; 6: 452-462
- 2017 Comprehensive update of the Canadian Cardiovascular Society Guidelines for the Management of Heart Failure.Can J Cardiol. 2017; 33: 1342-1433
- Why don't physicians follow clinical practice guidelines? A framework for improvement.JAMA. 1999; 282: 1458-1465
- Putting meaning into meaningful use: a roadmap to successful integration of evidence at the point of care.JMIR Med Inform. 2016; 4: e16
- Frailty assessment in the cardiovascular care of older adults.J Am Coll Cardiol. 2014; 63: 747-762
Article info
Publication history
Published online: July 25, 2019
Accepted:
July 15,
2019
Received:
May 13,
2019
Footnotes
See editorial by Heckman et al., pages 19–21 of this issue.
See page 90 for disclosure information.
Identification
Copyright
© 2019 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
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- The Role of Physicians in the Era of Big DataCanadian Journal of CardiologyVol. 36Issue 1