title: Patient selection for cardiac surgery: Time to consider subgroups within risk categories?
abstract: BACKGROUND
  Medical guidelines increasingly use risk stratification and implicitly assume that individuals classified in the same risk category form a homogeneous group, while individuals with similar, or even identical, predicted risks can still be very different. We evaluate a strategy to identify homogeneous subgroups typically comprising predicted risk categories to allow further tailoring of treatment allocation and illustrate this strategy empirically for cardiac surgery patients with high postoperative mortality risk.
  
  
  METHODS
  Using a dataset of cardiac surgery patients (n=6517) we applied cluster analysis to identify homogenous subgroups of patients comprising the high postoperative mortality risk group (EuroSCORE ≥ 15%). Cluster analyses were performed separately within younger (<75 years) and older (≥ 75 years) patients. Validity measures were calculated to evaluate quality and robustness of the identified subgroups.
  
  
  RESULTS
  Within younger patients two distinct and robust subgroups were identified, differing mainly in preoperative state and indication of recent myocardial infarction or unstable angina. In older patients, two distinct and robust subgroups were identified as well, differing mainly in preoperative state, presence of chronic pulmonary disease, previous cardiac surgery, neurological dysfunction disease and pulmonary hypertension.
  
  
  CONCLUSIONS
  We illustrated a feasible method to identify homogeneous subgroups of individuals typically comprising risk categories. This allows a single treatment strategy--optimal only on average, across all individuals in a risk category--to be replaced by subgroup-specific treatment strategies, bringing us another step closer to individualized care. Discussions on allocation of cardiac surgery patients to different interventions may benefit from focusing on such specific subgroups.
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands. Electronic address: a.vangiessen@umcutrecht.nl.
authors.forename: A
authors.initials: A
authors.lastname: van Giessen
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
authors.forename: G A
authors.initials: GA
authors.lastname: de Wit
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands.
authors.forename: H A
authors.initials: HA
authors.lastname: Smit
authors.affiliation: Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: H M
authors.initials: HM
authors.lastname: den Ruijter
authors.affiliation: Department of Anesthesiology and Intensive Care, Isala, Zwolle, the Netherlands.
authors.forename: A P
authors.initials: AP
authors.lastname: Nierich
authors.affiliation: Department of Cardiology, Isala, Zwolle, the Netherlands.
authors.forename: W W
authors.initials: WW
authors.lastname: Jansen Klomp
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands.
authors.forename: K G M
authors.initials: KG
authors.lastname: Moons
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands; Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands.
authors.forename: H
authors.initials: H
authors.lastname: Koffijberg
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D006348:Cardiac Surgical Procedures / Q000009:adverse effects / Q000379:methods* / Q000401:mortality; D000066491:Clinical Decision-Making / Q000379:methods; D016000:Cluster Analysis; D003362:Cost-Benefit Analysis; D005240:Feasibility Studies; D005260:Female; D006331:Heart Diseases / Q000145:classification / Q000175:diagnosis* / Q000601:surgery*; D006801:Humans; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D011183:Postoperative Complications / Q000209:etiology; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods
keywords: Cardiac surgery; Cluster analysis; Risk prediction; Subgroups; Tailored treatment