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RISK STRATIFICATION MODELS FOR HEART VALVE SURGERY
Author(s) -
Yap C. H.,
O’keefe M.,
Mohajeri M.,
Yii M.
Publication year - 2007
Publication title -
anz journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.426
H-Index - 70
eISSN - 1445-2197
pISSN - 1445-1433
DOI - 10.1111/j.1445-2197.2007.04115_15.x
Subject(s) - medicine , risk stratification , receiver operating characteristic , cohort , area under the curve , calibration , chi square test , risk assessment , demography , surgery , statistics , mathematics , computer security , sociology , computer science
Purpose Risk stratification models may be useful in aiding surgical decision‐making, preoperative informed consent, quality assurance and healthcare management. While several overseas models exist, no model has been well‐validated for use in Australia. We aimed to assess the performance of two valve surgery risk stratification models in an Australian patient cohort. Method The Society of Cardiothoracic Surgeons of Great Britain and Ireland (SCTS) and Northern New England (NNE) models were applied to all patients undergoing valvular heart surgery at St Vincent’s Hospital Melbourne and The Geelong Hospital between June 2001 and November 2006. Observed and predicted early mortalities were compared using the chi‐square test. Model discrimination was assessed by the area under the receiver operating characteristic (ROC) curve. Model calibration was tested by applying the chi‐square test to risk tertiles. Results SCTS model (n = 1095) performed well. Observed mortality was 4.84%, expected mortality 6.64% (chi‐square p = 0.20). Model discrimination (area under ROC curve 0.835) and calibration was good (chi‐square p = 0.9). the NNE model (n = 1015) over‐predicted mortality. Observed mortality 4.83% and expected 7.54% (chi‐square p < 0.02). Model discrimination (area under ROC curve 0.835) and calibration was good (chi‐square p = 0.9). Conclusion Both models showed good model discrimination and calibration. The NNE model over‐predicted early mortality whilst the SCTS model performed well in our cohort of patients. The SCTS model may be useful for use in Australia for risk stratification.