Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care

International Working Group on Chest Pain in Primary Care (INTERCHEST); Marc Aerts; Girma Minalu; Stefan Bösner; Frank Buntinx; Bernard Burnand; Jörg Haasenritter; Lilli Herzig; J André Knottnerus; Staffan Nilsson; Walter Renier; Carol Sox; Harold Sox; Norbert Donner-Banzhoff

doi:10.1016/j.jclinepi.2016.09.011

Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care

J Clin Epidemiol. 2017 Jan:81:120-128. doi: 10.1016/j.jclinepi.2016.09.011. Epub 2016 Oct 20.

Affiliations

¹ Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BIOSTAT), Hasselt University, I-BioStat, Agoralaan, Building D, Diepenbeek B-3590, Belgium. Electronic address: marc.aerts@uhasselt.be.
² Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BIOSTAT), Hasselt University, I-BioStat, Agoralaan, Building D, Diepenbeek B-3590, Belgium.
³ Department of General Practice and Family Medicine, Philipps University Marburg, Karl-von-Str. 4, Marburg 35037, Germany.
⁴ Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33, Blok J, PB 7001, Leuven 3000, Belgium; Department of General Practice, Maastricht University, Peter Debyeplein 1, P.O. Box 616, Maastricht 6200 MD, The Netherlands.
⁵ Institute of Social and Preventive Medicine, Lausanne University Hospital, Route de la Corniche 10, Lausanne 1010, Switzerland.
⁶ Institute of Family Medicine, University of Lausanne, 44 rue du Bugnon, Lausanne CH-1011, Switzerland.
⁷ Department of General Practice, Maastricht University, Peter Debyeplein 1, P.O. Box 616, Maastricht 6200 MD, The Netherlands.
⁸ Division of Community Medicine, Department of Medicine and Health Sciences, Linköping University, Linköping SE-581 83, Sweden.
⁹ Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33, Blok J, PB 7001, Leuven 3000, Belgium.
¹⁰ Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH 03755-1404, USA.
¹¹ Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH 03755-1404, USA; Patient-Centered Outcomes Research Institute, 1828 L Street, NW, Suite 900, Washington, DC 20036, USA.

PMID: 27773828
DOI: 10.1016/j.jclinepi.2016.09.011

Abstract

Objective: To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care.

Study design and setting: Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies.

Results: The most parsimonious rule included six equally weighted predictors: age ≥55 (males) or ≥65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is <2. The area under the ROC curve was 0.84. We applied this rule to a study setting with a CAD prevalence of 13.2% using a prediction score cutoff of <2 (i.e., -1, 0, or +1). When the score was <2, the probability of CAD was 2.1% (95% CI: 1.1-3.9%); when the score was ≥ 2, it was 43.0% (95% CI: 35.8-50.4%).

Conclusions: Clinical prediction rules are a key strategy for individualizing care. Large data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action.

Keywords: Chest pain; Individual patient data meta-analysis; Medical history taking; Myocardial ischemia; Primary health care; Sensitivity and specificity; Symptom assessment.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Chest Pain / etiology*
Coronary Artery Disease / complications*
Coronary Artery Disease / diagnosis*
Decision Support Techniques*
Female
Humans
Male
Middle Aged
Primary Health Care / methods*
ROC Curve
Sensitivity and Specificity