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Joseph S Ross,1–4 Jonathan Bates,4 Craig S Parzynski,4 Joseph G Akar,4,5 Jeptha P Curtis,4,5 Nihar R Desai,4,5 James V Freeman,4,5 Ginger M Gamble,4 Richard Kuntz,6 Shu-Xia Li,4 Danica Marinac-Dabic,7 Frederick A Masoudi,8 Sharon-Lise T Normand,9,10 Isuru Ranasinghe,11 Richard E Shaw,12 Harlan M Krumholz2–5 1Section of General Medicine, Department of Medicine, 2Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, 3Department of Health Policy and Management, Yale School of Public Health, 4Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, 5Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, 6Medtronic Inc, Minneapolis, MN, 7Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 8Division of Cardiology, Department of Medicine, University of Colorado, Aurora, CO, 9Department of Health Care Policy, Harvard Medical School, 10Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA; 11Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia; 12Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA Background: Machine learning methods may complement traditional analytic methods for medical device surveillance.Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=–0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=–0.042).Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. Keywords: implanted cardioverter–defibrillator, methodology, surveillance

BACKGROUND: Readmission following an acute coronary syndrome (ACS) is frequent in our community. Patient specific factors identifying those at risk of readmission are poorly described. METHODS: Data were analysed from 5219 patients with an ACS enrolled in the Australian and New Zealand population of the Global Registry of Acute Coronary Events (GRACE) between 1999 and 2007. Patients who were readmitted for cardiovascular disease within 6 months of discharge were identified; regression analysis was used to predict independent patient factors associated with readmission 1 month and 1-6 months after discharge. RESULTS: 1048 patients (20.1%) were readmitted within 6 months, with a significant proportion (n=434, 41.4%) of readmissions occurring within 30 days of discharge. Readmission within 6 months was associated with a higher incidence of unscheduled cardiac catheterisation (HR 25.64, 95% CI 18.41 to 35.71), unscheduled percutaneous coronary intervention (PCI) (HR 15.78, 95% CI 10.56 to 23.59), stroke (HR 1.92, 95% CI 1.08 to 3.43), and death (HR 2.40, 95% CI 1.66 to 3.49). Recurrent ischemia in hospital and a diagnosis of S-T elevation myocardial infarction during the index admission were associated with the strongest risk of early rehospitalisation, while revascularisation by PCI or coronary artery bypass surgery (CABG) was associated with lowest risk of early readmission. A history of heart failure, prior myocardial infarction or angina was associated with a greater likelihood of later rehospitalisation, whereas revascularisation by CABG was associated with the lowest risk of later rehospitalisation. CONCLUSIONS: Several patient and clinical factors identify patients at higher risk of readmission. Identifying these factors and escalating in-hospital and post-discharge care for these higher risk patients may prevent readmission and improve outcome. [ABSTRACT FROM AUTHOR]

Objectives: To describe temporal trends in the use of evidence-based medical therapies and management of patients with acute coronary syndromes (ACS) in Australia and New Zealand.Design, Setting and Participants: Our analysis of the Australian and New Zealand cohort of the Global Registry of Acute Coronary Events (GRACE) included patients with ST-segment-elevation myocardial infarction (STEMI) and non-ST-segment-elevation ACS (NSTEACS) enrolled continuously between January 2000 and December 2007 from 11 metropolitan and rural centres in Australia and New Zealand.Results: 5615 patients were included in this analysis (1723 with STEMI; 3892 with NSTEACS). During 2000-2007 there was an increase in the use of statin therapy, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and thienopyridines (P < 0.0001 for each). Among patients with STEMI, there was an increase in emergency revascularisation with PCI (from 11% to 27% [P < 0.0001]), and inhospital coronary angiography (from 61% to 76% [P < 0.0001]). Among patients with NSTEACS, there was an increase in revascularisation with PCI (from 20% to 25% [P = 0.004]). Heart failure rates declined substantially among STEMI and NSTEACS patients (from 21% to 12% [P = 0.0002], and from 13% to 4% [P < 0.0001], respectively) as did rates of hospital readmission for ischaemic heart disease at 6 months (from 23% to 9% [P = 0.0001], and from 24% to 15% [P = 0.0001], respectively).Conclusions: From 2000 to 2007 in Australia and New Zealand, there was a fall in inhospital events and 6-month readmissions among patients admitted with ACS. This showed an association with improved uptake of guideline-recommended medical and interventional therapies. These data suggest an overall improvement in the quality of care offered to contemporary ACS patients in Australia and New Zealand. [ABSTRACT FROM AUTHOR]