A review of valve surgery for rheumatic heart disease in Australia.

BACKGROUND: Globally, rheumatic heart disease (RHD) remains an important cause of heart disease. In Australia it particularly affects older non-Indigenous Australians and Aboriginal Australians and/or Torres Strait Islander peoples. Factors associated with the choice of treatment for advanced RHD remain variable and poorly understood. METHODS: The Australian and New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database was analysed. Demographics, co-morbidities, pre-operative status and valve(s) affected were collated and associations with management assessed. RESULTS: Surgical management of 1384 RHD and 15843 non-RHD valve procedures was analysed. RHD patients were younger, more likely to be female and Indigenous Australian, to have atrial fibrillation (AF) and previous percutaneous balloon valvuloplasty (PBV). Surgery was performed on one valve in 64.5%, two valves in 30.0% and three valves in 5.5%. Factors associated with receipt of mechanical valves in RHD were AF (OR 2.69) and previous PBV (OR 1.98) and valve surgery (OR 3.12). Predictors of valve repair included being Indigenous (OR 3.84) and having fewer valves requiring surgery (OR 0.10). Overall there was a significant increase in the use of mitral bioprosthetic valves over time. CONCLUSIONS: RHD valve surgery is more common in young, female and Indigenous patients. The use of bioprosthetic valves in RHD is increasing. Given many patients are female and younger, the choice of valve surgery and need for anticoagulation has implications for future management of RHD and related morbidity, pregnancy and lifestyle plans.

Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression.

BACKGROUND: Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. METHODS: The Australasian Society of Cardiac and Thoracic Surgeons (ASCTS) registry provided data on 32,354 patients undergoing cardiac surgery in 18 hospitals from 2001 to 2009. The logistic regression methods related 30-day mortality after surgery to hospital characteristics with concurrent adjustment for patient characteristics. RESULTS: Hospital-level mortality rates varied from 1.0% to 4.1% of patients. Ordinary, marginal and multilevel regression methods differed with regard to point estimates and conclusions on statistical significance for hospital-level risk factors; ordinary logistic regression giving inappropriately narrow confidence intervals. The median odds ratio, MOR, from the multilevel model was 1.2 whereas ORs for most patient-level characteristics were of greater magnitude suggesting that unexplained between-hospital variation was not as relevant as patient-level characteristics for understanding mortality rates. For hospital-level characteristics in the multilevel model, 80% interval ORs, IOR-80%, supplemented the usual ORs from the logistic regression. The IOR-80% was (0.8 to 1.8) for academic affiliation and (0.6 to 1.3) for the median annual number of cardiac surgery procedures. The width of these intervals reflected the unexplained variation between hospitals in mortality rates; the inclusion of one in each interval suggested an inability to add meaningfully to explaining variation in mortality rates. CONCLUSIONS: Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios.

Prediction of acute kidney injury within 30 days of cardiac surgery.

OBJECTIVE: To predict acute kidney injury after cardiac surgery. METHODS: The study included 28,422 cardiac surgery patients who had had no preoperative renal dialysis from June 2001 to June 2009 in 18 hospitals. Logistic regression analyses were undertaken to identify the best combination of risk factors for predicting acute kidney injury. Two models were developed, one including the preoperative risk factors and another including the pre-, peri-, and early postoperative risk factors. The area under the receiver operating characteristic curve was calculated, using split-sample internal validation, to assess model discrimination. RESULTS: The incidence of acute kidney injury was 5.8% (1642 patients). The mortality for patients who experienced acute kidney injury was 17.4% versus 1.6% for patients who did not. On validation, the area under the curve for the preoperative model was 0.77, and the Hosmer-Lemeshow goodness-of-fit P value was .06. For the postoperative model area under the curve was 0.81 and the Hosmer-Lemeshow P value was .6. Both models had good discrimination and acceptable calibration. CONCLUSIONS: Acute kidney injury after cardiac surgery can be predicted using preoperative risk factors alone or, with greater accuracy, using pre-, peri-, and early postoperative risk factors. The ability to identify high-risk individuals can be useful in preoperative patient management and for recruitment of appropriate patients to clinical trials. Prediction in the early stages of postoperative care can guide subsequent intensive care of patients and could also be the basis of a retrospective performance audit tool.

A preoperative risk prediction model for 30-day mortality following cardiac surgery in an Australian cohort.

BACKGROUND: Population-specific risk models are required to build consumer and provider confidence in clinical service delivery, particularly when the risks may be life-threatening. Cardiac surgery carries such risks. Currently, there is no model developed on the Australian cardiac surgery population and this article presents a novel risk prediction model for the Australian cohort with the aim to provide a guide for the surgeons and patients in assessing preoperative risk factors for cardiac surgery. AIMS: This study aims to identify preoperative risk factors associated with 30-day mortality following cardiac surgery for an Australian population and to develop a preoperative model for risk prediction. METHODS: All patients (23016) undergoing cardiac surgery between July 2001 and June 2008 recorded in the Australian Society of Cardiac and Thoracic Surgeons (ASCTS) database were included in this analysis. The data were divided randomly into model creation (13810, 60%) and model validation (9206, 40%) sets. The model was developed on the creation set and then validated on the validation set. The bootstrap sampling and automated variable selection methods were used to develop several candidate models. The final model was selected from this group of candidate models by using prediction mean square error (MSE) and Bayesian Information Criteria (BIC). Using a multifold validation, the average receiver operating characteristic (ROC), p-value for Hosmer-Lemeshow chi-squared test and MSE were obtained. Risk thresholds for low-, moderate- and high-risk patients were defined. The expected and observed mortality for various risk groups were compared. The multicollinearity and first-order interaction effect between clinically meaningful risk factors were investigated. RESULTS: A total of 23016 patients underwent cardiac surgery and the 30-day mortality rate was 3.2% (728 patients). Independent predictors of mortality in the model were: age, sex, the New York Heart Association (NYHA) class, urgency of procedure, ejection fraction estimate, lipid-lowering treatment, preoperative dialysis, previous cardiac surgery, procedure type, inotropic medication, peripheral vascular disease and body mass index (BMI). The model had an average ROC 0.8223 (95% confidence interval (CI): 0.8118-0.8227), p-value 0.8883 (95% CI: 0.8765-0.90) and MSE 0.0251 (95% CI: 0.02515-0.02516). The validation set had observed mortality 3.0% (95% CI: 2.7-3.3%) and predicted mortality 2.9% (95% CI: 2.6-3.2%). The low-risk group (additive score 0-3) had 0.6% observed mortality (95% CI: 0.3-0.9%) and 0.5% predicted mortality (95% CI: 0.2-0.8%). The moderate-risk group (additive score 4-9) had 1.7% observed mortality (95% CI: 1.2-2.2%) and 1.4% predicted mortality (95% CI: 1.0-1.8%). The observed mortality for the high-risk group (additive score 9 plus) was 6.7% (95% CI: 5.8-7.6%) and the expected mortality was 6.7% (95% CI: 5.8-7.6%). CONCLUSION: A preoperative risk prediction model for 30-day mortality was developed for the Australian cardiac surgery population.