Background— Perioperative myocardial infarction or cardiac arrest is associated with significant morbidity and mortality. The Revised Cardiac Risk Index is currently the most commonly used cardiac risk stratification tool; however, it has several limitations, one of which is its relatively low discriminative ability. The objective of the present study was to develop and validate a predictive cardiac risk calculator. Methods and Results— Patients who underwent surgery were identified from the American College of Surgeons' 2007 National Surgical Quality Improvement Program database, a multicenter (>250 hospitals) prospective database. Of the 211 410 patients, 1371 (0.65%) developed perioperative myocardial infarction or cardiac arrest. On multivariate logistic regression analysis, 5 predictors of perioperative myocardial infarction or cardiac arrest were identified: type of surgery, dependent functional status, abnormal creatinine, American Society of Anesthesiologists' class, and increasing age. The risk model based on the 2007 data set was subsequently validated on the 2008 data set (n=257 385). The model performance was very similar between the 2007 and 2008 data sets, with C statistics (also known as area under the receiver operating characteristic curve) of 0.884 and 0.874, respectively. Application of the Revised Cardiac Risk Index to the 2008 National Surgical Quality Improvement Program data set yielded a relatively lower C statistic (0.747). The risk model was used to develop an interactive risk calculator. Conclusions— The cardiac risk calculator provides a risk estimate of perioperative myocardial infarction or cardiac arrest and is anticipated to simplify the informed consent process. Its predictive performance surpasses that of the Revised Cardiac Risk Index.
Introduction The aim of this study was to assess the impact of home-based telehealth monitoring on health outcomes, quality of life and costs over 12 months for patients with diabetes and/or chronic obstructive pulmonary disease (COPD) who were identified as being at high risk of readmission to hospital. Methods This pilot study was a randomised controlled trial combined with an economic analysis to examine the outcomes of standard care versus home-based telehealth for people with diabetes and/or COPD who were at risk of hospital readmission within one year. The primary outcomes were (i) hospital admission and length of stay (LOS); and (ii) health-related quality of life (HRQOL); and the secondary outcomes were (i) health-related clinical outcomes; (ii) anxiety and depression scores; and (iii) health literacy. The costs of the intervention and hospitalisations were included. Results A total of 86 and 85 participants were randomised to the intervention and control groups respectively. The difference between groups in hospital LOS was -3.89 (95% confidence interval (CI): -9.40, 1.62) days, and for HRQOL, 0.09 (95% CI: 0.05, 0.14) in favour of the telehealth monitoring group. There was a saving of AUD$6553 (95% CI: -12145, -961) in the cost of hospitalisation over 12 months, which offset the increased cost of tele-monitoring. The intervention group showed an improvement in anxiety, depression and health literacy at 12 months, and in the diabetes group, a reduction in microalbuminuria. Discussion The telehealth monitoring intervention improved patient's health outcomes and quality of life at no additional cost.
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