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AIMS: Acute aortic dissection (AD) requires immediate treatment, but is a diagnostic challenge. We studied how often AD was missed initially, which patients were more likely to be missed and how this influenced patient management and outcomes. METHODS: A retrospective cohort study including 200 consecutive patients with AD as the final diagnosis, admitted to a tertiary hospital between 1998 and 2008. The first differential diagnosis was identified and patients with and without AD included were compared. Characteristics associated with a lower level of suspicion were identified using multivariable logistic regression, and Cox regression was used for survival analyses. Missing data were imputed. RESULTS: Mean age was 63 years, 39% were female and 76% had Stanford type A dissection. In 69% of patients, AD was included in the first differential diagnosis; this was less likely in women (adjusted relative risk [aRR]: 0.66, 95% CI: 0.44-0.99), in the absence of back pain (aRR: 0.51, 95% CI: 0.30-0.84), and in patients with extracardiac atherosclerosis (aRR: 0.64, 95% CI: 0.43-0.96). Absence of AD in the differential diagnosis was associated with the use of more imaging tests (1.8 vs. 2.3, p = 0.01) and increased time from admission to surgery (1.8 vs. 10.1 h, p < 0.01), but not with a difference in the adjusted long-term all-cause mortality (hazard ratio: 0.76, 95% CI: 0.46-1.27). CONCLUSION: Acute aortic dissection was initially not suspected in almost one-third of patients, this was more likely in women, in the absence of back pain and in patients with extracardiac atherosclerosis. Although the number of imaging tests was higher and time to surgery longer, patient outcomes were similar in both groups.
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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.