Patient registries of acute coronary syndrome: assessing or biasing the clinical real world data?
| Autor: | José Cuñat, Jaume Marrugat, Fernando Arós, Josep Ramon Marsal, Núria Soriano, Purificación Cascant, Gaietà Permanyer-Miralda, Francesca Mitjavila, Aida Ribera, Magda Heras, Pedro L. Sánchez, Ignacio Ferreira-González, Emilia Civeira, Héctor Bueno, Antoni Parada |
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| Rok vydání: | 2009 |
| Předmět: |
Quality Control
Acute coronary syndrome Pediatrics medicine.medical_specialty media_common.quotation_subject Initial sample Population Audit Risk Assessment Medicine Humans Hospital Mortality Registries Acute Coronary Syndrome education Selection Bias media_common Aged Selection bias education.field_of_study Clinical Audit business.industry Patient Selection Middle Aged medicine.disease Spain Relative risk Cardiology and Cardiovascular Medicine business Real world data |
| Zdroj: | Circulation. Cardiovascular quality and outcomes. 2(6) |
| ISSN: | 1941-7705 |
| Popis: | Background— The risk of selection bias in registries and its consequences are relatively unexplored. We sought to assess selection bias in a recent registry about acute coronary syndrome and to explore the way of conducting and reporting patient registries of acute coronary syndrome. Methods and Results— We analyzed data from patients of a national acute coronary syndrome registry undergoing an audit about the comprehensiveness of the recruitment/inclusion. Patients initially included by hospital investigators (n=3265) were compared to eligible nonincluded (missed) patients (n=1439). We assessed, for 25 exposure variables, the deviation of the in-hospital mortality relative risks calculated in the initial sample from the actual relative risks. Missed patients were of higher risk and received less recommended therapies than the included patients. In-hospital mortality was almost 3 times higher in the missed population (9.34% [95% CI, 7.84 to 10.85] versus 3.9% [95% CI, 2.89 to 4.92]). Initial relative risks diverged from the actual relative risks more than expected by chance ( P Conclusions— Irregular inclusion can introduce substantial systematic bias in registries. This problem has not been explicitly addressed in a substantial number of them. |
| Databáze: | OpenAIRE |
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