No abstract

C ardiovascular diseases (CVDs) are the greatest contributors to the global burden of disease, and finding ways to reduce this burden are a major challenge faced by health systems worldwide.1 Most guidelines recommend that the decision to use vascular disease preventive drug therapy should be on the basis of a patient's overall or absolute cardiovascular risk. 2 The broader application of risk-based care with safe, effective treatments has the potential to reduce disease burden substantially Background-Despite effective treatments to reduce cardiovascular disease risk, their translation into practice is limited. Methods and Results-Using a parallel arm cluster-randomized controlled trial in 60 Australian primary healthcare centers, we tested whether a multifaceted quality improvement intervention comprising computerized decision support, audit/ feedback tools, and staff training improved (1) guideline-indicated risk factor measurements and (2)

PURPOSE For patients with primary cutaneous melanoma, the risk of sentinel node (SN) metastasis varies according to several clinicopathologic parameters. Patient selection for SN biopsy can be assisted by National Comprehensive Cancer Network (NCCN) and ASCO/Society of Surgical Oncology (SSO) guidelines and the Memorial Sloan Kettering Cancer Center (MSKCC) online nomogram. We sought to develop an improved online risk calculator using alternative clinicopathologic parameters to more accurately predict SN positivity. PATIENTS AND METHODS Data from 3,477 patients with melanoma who underwent SN biopsy at Melanoma Institute Australia (MIA) were analyzed. A new nomogram was developed by replacing body site and Clark level from the MSKCC model with mitotic rate, melanoma subtype, and lymphovascular invasion. The predictive performance of the new nomogram was externally validated using data from The University of Texas MD Anderson Cancer Center (n = 3,496). RESULTS The MSKCC model receiver operating characteristic curve had a predictive accuracy of 67.7% (95% CI, 65.3% to 70.0%). The MIA model had a predictive accuracy of 73.9% (95% CI, 71.9% to 75.9%), a 9.2% increase in accuracy over the MSKCC model ( P < .001). Among the 2,748 SN-negative patients, SN biopsy would not have been offered to 22.1%, 13.4%, and 12.4% based on the MIA model, the MSKCC model, and NCCN or ASCO/SSO criteria, respectively. External validation generated a C-statistic of 75.0% (95% CI, 73.2% to 76.7%). CONCLUSION A robust nomogram was developed that more accurately estimates the risk of SN positivity in patients with melanoma than currently available methods. The model only requires the input of 6 widely available clinicopathologic parameters. Importantly, the number of patients undergoing unnecessary SN biopsy would be significantly reduced compared with use of the MSKCC nomogram or the NCCN or ASCO/SSO guidelines, without losing sensitivity. An online calculator is available at www.melanomarisk.org.au .